EXPERIMENTAL TESTING OF WAVE TRANSMISSION COEFFICIENTS FOR OYSTER SHELL-FILLED BAG BERMS
Bibliographic record
Abstract
Climate change and rising sea levels pose a significant risk of extreme flooding and erosion in coastal zones, requiring adaptation to make the shorelines more resilient. Berms composed of oyster shell filled-bags have been implemented at shorelines in western Canada (Provan et al., 2023) and in the United States (Milligan et al., 2018; Spiering et al., 2021; Wellman et al., 2021, among others) to provide a nature-based solution to reduce shoreline erosion or help stabilize restored salt marshes. However, there is limited available information on the performance and ability of these oyster shell berms in terms of wave attenuation. Previous experimental studies have been conducted to address this lack of information (Allen and Web, 2011; Coghlan et al., 2017); however, the studies were limited in tested wave conditions and sizes of the tested oyster shell filled-bags. To address this, a series of full-scale (1:1) physical model experiments were carried out to investigate the wave attenuation ability of oyster shell bags for different bag sizes and configurations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".